Large Covariance Matrices: Accurate Models Without Mocks

TL;DR

This paper introduces a method to accurately estimate cosmological covariance matrices using model-based approaches calibrated directly from survey data, eliminating the need for extensive mock catalogues.

Contribution

It demonstrates that model covariance matrices can be calibrated from data alone using jackknife methods, reducing reliance on computationally expensive mocks.

Findings

Model covariance matrices can be calibrated with data-only methods.

Jackknife techniques provide consistent parameter estimates from a single survey.

The approach reduces computational resources needed for covariance estimation.

Abstract

Covariance matrix estimation is a persistent challenge for cosmology. We focus on a class of model covariance matrices that can be generated with high accuracy and precision, using a tiny fraction of the computational resources that would be required to achieve comparably precise covariance matrices using mock catalogues. In previous work, the free parameters in these models were determined using sample covariance matrices computed using a large number of mocks, but we demonstrate that those parameters can be estimated consistently and with good precision by applying jackknife methods to a single survey volume. This enables model covariance matrices that are calibrated from data alone, with no reference to mocks.